Browsing by Author "Ma, CW"

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Detection of coherent signals using weighted subspace smoothing
(1996-02-01)
Ma, CW
;
Teng, CC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
A new approach to detecting the number of coherent signals incident upon a uniformly-spaced linear array is presented. The approach combines a modified spatial smoothing scheme and a modified MDL criterion. The modified spatial smoothing scheme, referred to as ''weighted subspace smoothing,'' is actually a generalization of the ''post-smoothing'' approach proposed by Krim and Proakis in 1994. It is shown that the noise eigenvalues obtained with weighted subspace smoothing are more accurate than those obtained with the original spatial smoothing. We thus attempt to improve detection performance using these more accurate eigenvalues. A novel modification of the minimum description length (MDL) criterion is proposed to accomplish this. Computer simulations are presented to study the performance improvement of this new approach.
Fuzzy neural network approach for 2-D direction finding in multipath environments
(1999-04-01)
Ma, CW
;
Teng, CC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A fuzzy neural network (FNN) approach for estimating the two-dimensional (2-D) direction of a radiating source in coherent multipath environments via a 2-D passive sensor array is presented. Outputs of the array are preprocessed by a judiciously constructed reference-point preprocessing scheme to produce proper inputs for an FNN. The FNN then uses these preprocessed outputs to produce the estimate of the source direction. Since the preprocessed outputs preserve enough information about the source direction and the FNN maps the preprocessed outputs into the source direction with high accuracy, the FNN approach exhibits good estimation performance. By computer simulations, it is found that the FNN approach drastically outperforms the spatial smoothing 2-D MUSIC algorithm.
Near-field direction finding with a fuzzy neural network
(1996)
Ma, CW
;
Teng, CC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
The single-source near-field direction finding problem can be solved by a fuzzy nerual network (FNN). The FNN approach can be applied to arrays with arbitrary configurations. It can also be implemented for real-time tracking applications. The approach outperforms the far-field approximation (FFA) approach when the array is uniformly-spaced and Linear, especially when the angle between the array normal direction and the source direction is large and the distance from array center to the source is short.
Tracking a near-field moving target using fuzzy neural networks
(2000-03-16)
Ma, CW
;
Teng, CC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper we explore the problem of tracking a near-field moving target using fuzzy neural networks (FNNs). The moving target radiates narrow band waves that impinge on an array of passive sensors. At a particular time instance, the location of the target is estimated by several judiciously constructed FNN-based angle and distance estimators. When the target is moving, its trajectory can be on-line estimated due to the parallel and real-time computational capability of the FNNs. Computer simulation results illustrate the performance of the FNN-based angle estimator, distance estimator, and the near-field moving target tracker. (C) 2000 Elsevier Science B.V. All rights reserved.